In this work, we investigate whether attention-based attribution maps in a vision transformer provide causal explanations for diabetic retinopathy (DR) classification. We introduce a principled decomposition of attention into two attribution maps: the contribution ($c_t$), which satisfies the completeness axiom in explainable artificial intelligence (xAI), and the pull ($p_t$), which quantifies causal explanations. We define an identity relating $c_t$ and $p_t$ that characterizes when $c_t$ fails to capture causality. We test whether each map is causally faithful on the MAPLES-DR dataset by dropping/inserting tokens, and report Necessity ($N$), Sufficiency ($Su$), and Faithfulness Correlation ($FC$). The pull $p_t$ achieves the highest causal alignment ($FC = 0.53$, $N = 0.71\%$, $Su = 3.0\%$), outperforming Attention Rollout, Grad-CAM, and AttnLRP. Occlusion retains better sufficiency ($Su = 1.6\%$) but is about 800 times slower. Furthermore, on MAPLES-DR, $p_t$ achieves $70\%$ of the average precision (AP) of HRDecoder, a state-of-the-art supervised lesion segmentation model, despite the absence of pixel-level supervision. Together, these findings show that the pull $p_t$ and the contribution $c_t$ form computationally efficient attribution maps for reliable causal interpretation in safety-critical medical diagnostics. The code and demo are available at https://github.com/pramath-haritz/dr-xai.

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